from __future__ import absolute_import, division, print_function, unicode_literals
# !pip install -q tensorflow==2.0.0-alpha0
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib

mnist = tf.keras.datasets.mnist
 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])
 
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
 
model.evaluate(x_test, y_test)
